Maejo International Journal of Energy and Environmental
Communication
ARTICLE
Influential degree of polymerization of sugar extraction on alkali pretreatment from sunflower stalk wastes by applied statistical modelling
Numchok Manmai 1*, Nuttapong Saetang2, Yuwalee Unpaprom3, Keng-Tung Wu 1,*
1Department of Foreatry, National Chung Hsing UniversityNCHU), Taichung 402, Taiwan.
2Center of Excellence in Agricultural Innovation for Graduate Entrepreneur, Maejo University, Chiang Mai, 50290, Thailand
3Program in Biotechnology, Faculty of Science, Maejo University, Chiang Mai, 50290, Thailand
*Corresponding author, E-mail address: [email protected] , [email protected]
ARTICLE INFOR
Received 21st June 2019 Accepted 12th August 2019
Keywords:
Sunflower stalk
Sodium hydroxide pretreatment Response surface methodology (RSM) Central composite design (CCD) Degree of polymerization (DP)
1. Introduction
In addition energy has been needed for the growing demand and the development of the world Fossil fuels are the one source of energy the world. However, the use of fossil fuels is related with numerous environmental problems that affect the entire countries and the people (Vu et al., 2017).
Nowadays, due to the high consumption, it is close to natural limits and takes a long time to be created (Vu et al., 2017). In consequence, biofuel from biomass therefore is an important alternative to substitute for primary energy. Lignocellulosic is a talented bioprocess substrate for the invention of biofuels
because agricultural biomass are organized a possible source of low-cost material for the bioenergy construction (Manmai et al., 2019a). It is one of the most plentiful renewable resources for value-added chemicals. The construction of bioethanol from lignocellulosic has involved worldwide interest (Manmai et al., 2017a).
The three classifications of biomaterials for being bioethanol substrates are sugar (monosaccharides and disaccharides), starchy (reserve polysaccharides), and lignocellulosic (structural polysaccharides) crops (Bautista et al., 2019). Bioethanol is a type of bioenergy with good for This paper presents application of influence of degree of polymerization (DP) on optimally pretreated using a response surface methodology (RSM) approach for decreased DP level of optimal chemical and biological pretreatments from sunflower stalk. All experiments in this paper are applied by statistical designs for developing a statistic multifunction analysis model that focus on the effect of dissimilar factors for describing of the optimum values of the changed surface response on any variables. The process parameters of chemical model (Sodium Hydroxide concentration and Time) to pretreat for DP. The chemical pretreatment model was certified by 13 runs, at two factors, NaOH (1, 1.5, 2%) and Day (1, 2, 3) by central composite design (CCD). DP value of the chemical model was estimated by a Design Expert program version 11 trial: chemical model of DP highest and lowest of 25.80 and 6.16, consecutively. The aim of this experiments to investigate only DP from the chemical model of pretreatment. The procedure there are effective on sugar conversion and DP of the lignocellulosic biomass. Which pretreatment is a challenge for cost and competitive technology on large-scale of fermentable sugar in the step of hydrolysis.
ABSTRACT
environmental, signifies a reassuring biofuel in current which is ordinarily used in blending with oil (Manmai et al., 2017b).
Nomenclature and Abbreviation RS Reducing sugar
TS Total sugar
RSM Response surface methodology CCD Central composite design SS Sunflower stalk
DP Degree of polymerization
It can be produced from different types of agricultural renewable materials such as sunflower stalk, sorghum stalk, sugarcane leaf and con stalk in a fiber part (Manmai et al., 2017c; Manmai et al., 2018a; Manmai et al., 2018b) and for weedy feedstocks are gooseweed and small- flowered nutsedge (Vu PT et al., 2017; Vu et al., 2018;
Ramaraj and Unpaprom 2019a; Ramaraj and Unpaprom, 2019b) . In the same direction corn stalk juice and pineapple
fruit peel wastes used for ethanol producing (Bautista et al., 2019; Casabar et al., 2019).
The major benefits of biofuels are presented in (Balat, 2011).
The using of biofuels can help to reduce greenhouse gas emissions (Tran et al., 2019), provide clean and sustainable energy sources, and increase the agricultural profits for poor people from developing countries. However, there may be other socio-economic and environmental impacts that affect the potential of developing countries that will benefit from the increased demand for biofuels worldwide.
Carbohydrates consists of carbon, hydrogen and oxygen. The minutest carbohydrate structures are monosaccharides, it is different types of polymerization, forming complex sugars, starches, and fibers. The carbohydrates are classified agreeing to chemical-structural properties, considered of degree of polymerization, in addition to classification of the carbohydrates by physiological properties. The degree of polymerization relates the numeral of monomers in a molecule. The classification is categorized carbohydrates into four groups: sugars, oligosaccharides, polysaccharides, and polyols. The chemical structural classification was presented in Table 1 from (Scapin et al., 2017).
Table 1. Chemical structural classification of the carbohydrates by the degree of polymerization (Scapin et al., 2017).
Classification DP Subgroup Components
Sugars 1 Monosaccharides Glucose, galactose, fructose 2 Disaccharides Sucrose, lactose, maltose, trehalose Oligosaccharides 3-9 Malto-oligosaccharides Maltodextrin
Other oligosaccharides Raffinose, stachyose, fructooligosaccharide Polysaccharides >9
Starch Amylose, amylopectin, modified starches Non-starch
polysaccharides Cellulose, hemicellulose, pectins, ydrocolloids Polyols 1->9 - Erythritol, xylitol, mannitol, sorbitol, maltitol,
isomalt, lactitol, polyglycol.
Sodium hydroxide (NaOH) pretreatment is the one of the most standard procedures and it is widely studied in bioconversion of lignocellulose, it is extremely effective to increase the degradability of hardwoods and agricultural waste materials with low lignin concentrations. (Bali et al., 2015).
Response surface methodology (RSM) is frequently organized for model to evaluate a procedure to study the relation among several independent factors and one or more response and the optimization of a process (Ramaraj and Unpaprom, 2019a). The design of new trials and utilizing concurrent optimization protocols, including the central composite design (CCD) that the two-level factorial design with additional points (star points) and at least one central point. The efficiency of the method with a minimum run of trials (Bagheri et al., 2019).
In this study investes the optimized DP value that changes with time and the concentration of NaOH by using statistical modelling to explane changed value. This study focused on using diluted NaOH pretreatment procedures which embraces time and NaOH concentration. The accountable properties of input parameters on the product of DP value are inspected. Furthermore, an initial assessment of the use of these DP predictes condition for hydrolysis and bioethanol production in the future.
2. Materials and methods 2.1 Resources preparation
Sunflower Stalks (SS) with Leave were gathered after seed harvesting in Figure 1(c) from the farm of Program in Agronomy, Faculty of Agricultural Production, Maejo
University, Chiang Mai, Thailand (18° 8’ 98” N 99°0’ 13” E) in Figure 1(a). Fished SS in Figure 1(b) were dried in a solar drying house was showed in Figure 1(d) among ambient temperature at Chiang Mai (30–32 ºC) for 3 days, the first time of drying. The resources were reduced size two steps by cutting and blending machines. Dried and powdered SS was
dehydrated again for evaporation moisture and water in resources by a hot air oven at 60 ºC for 48 h and stored in plastic bags at room temperature until further using. All resources preparation was adopted from (Manmai et al., 2019a; Manmai et al., 2019b; Manmai et al., 2020).
Figure 1. (a) Sunflower farm, (b) Fished sunflower stalk, (c) Seed and inflorescence (d) Dried stalk 2.2 Experimental design
In the prevent researches in 2019 and 2020, Manmai et al. presented the model for total sugar (TS) and reducing sugar (RS) were pretreated by NaOH (1, 1.5 and 2 % w/v) and T. reesei (1, 1.5 and 2 % v/v), which these research is series of two researches. The parameters of these study are used for DP of biomass pretreatment by chemical method consists of concentration of NaOH (%w/v, ANaOH) and time (day, BNaOH). All of factors were used as independent factors and three investigational levels: − 1, 0, 1. The range and center point values of the three independent factors were chosen after a series of preliminary single factor experiments in Table 2. A standard responsive surface design (RSM) is the central composite design (CCD). The RSM method is suitable for quadratic surface adjustment to optimize process factors with a minimum run of experiments, including analyzing the collaboration between parameters.
Table 2. Degree of polymerization parameters and their values used for experiment (Manmai et al., 2020) Factors Symbols Unit -1 0 1
NaOH ANaOH %(w/v) 1 1.5 2
Time BNaOH days 1 2 3
2.3 Analytical methods
Reducing sugar concentration was estimated by 3,5- dinitrosalicylic acid (DNS) method The absorbance was measured with a UV-Spectrophotometer detector DV-8000 (Drawell, Osaka, Japan) at 540 nm, using blank as control (Miller, 1959).
Total sugar concentration was analysed by phenol sulfuric methods at Spectrophotometer detector DV-8000 (Drawell, Osaka, Japan) at 490 nm (Dubois et al., 1956).
Colorimetric method for determination of sugars and related substances. Degree of polymerization is calculated by total sugar concentration devises reducing sugar concentration.
3. Results and discussion 3.1. Composition of sunflower stalk
In this research, sunflower stalks contain the following composition: (dry basis), glucan 33.99 ± 0.15%, xylan 24.1 ± 0.11%, arabinan 0.96 ± 0.00%, and lignin 25.9
±0.17%. Almost similar results of untreated sunflower stalks were reported in Hesami et al..
3.2. The equation in terms of coded
DP= 11.913 - 7.4799A - 2.2669B + 1.147AB + 3.180A2 + 0.068B2 (1)
The coded in Equation 1 is used to predict about the response for given levels of each factor. The coded equation is
beneficial for classifying the relative impact of the factors by comparing the factor coefficients.
3.3. ANOVA of NaOH pretreatment
The suitability of the models was observed by analysis of variance (ANOVA) and the results are showed in Table 3.
The model, A-NaOH, B-Time, AB and A² F-values of 91.26, 378.2, 34.74, 5.92, 31.47of DP respectively, it means that the model is significant. Only B2 is not signified of 0.0144.
There are only a 0.01% accidental that an F-value this large could occur due to noise.
P-values less than 0.05 are designated model terms are fit. In this model A, B, AB, A² are significant model terms.
And in this model there is only one value greater than 0.1 indicate the B2 terms are not significant.
The Lack of Fit F-value of DP from NaOH pretreatment is 0.89. It indicates the Lack of Fit is not significant relation to the pure error. It means that the model to fit.
The model of DP there are identical percent of C.V., R2, adjusted R2 and predicted R2 as 7.02, 0.9849, 0.9741 and 0.9453, respectively. The Predicted R² of 0.9453 is in realistic arrangement with the Adjusted R² of 0.9741 because the difference value is less than 0.2.
Adeq Precision is measured the signal to noise ratio.
A ratio greater than 4 is desirable. The model ratio of 30.456 indicates an acceptable signal. This model can be used to navigate the design space.
In Table 4 showed to finish the number of experimental runs of alkali pretreatment beside the predicted and observed responses of DP.
Table 3. ANOVA model for optimization of DP from NaOH pretreatment of sunflower stalk Source Sum of Squares df Mean F-value p-value
Model 405.01 5 81 91.26 < 0.0001 significant
A-NaOH 335.7 1 335.7 378.2 < 0.0001
B-Time 30.83 1 30.83 34.74 0.0006
AB 5.26 1 5.26 5.92 0.0452
A² 27.93 1 27.93 31.47 0.0008
B² 0.0128 1 0.0128 0.0144 0.9079
Residual 6.21 7 0.8876
Lack of Fit 2.48 3 0.8271 0.8864 0.5203 not significant
Pure Error 3.73 4 0.933
Cor Total 411.22 12
Pure Error 0.1705 4 0.0426
Cor Total 12.45 12
Std. Dev. 0.9421 R² 0.9849
Mean 13.41 Adjusted R² 0.9741
C.V. % 7.02 Predicted R² 0.9453
Adeq Precision 30.4562
Table 4. Designed runs with actual and predicted values of NaOH pretreatment of sunflower stalk
Std. Run
Factor 1 Factor 2 Respond 1:
DP A:NaOH (%) B:Time (Days) Actual
Value Predicted
Value Residual
8 1 1.5 3 8.33 7.61 0.7129
10 2 1.5 2 11.83 11.91 -0.0862
1 3 1 1 10.45 9.71 0.7354
9 4 1.5 2 6.16 6.56 -0.3994
5 5 1 2 10.96 11.91 -0.9544
6 6 2 2 10.4 11.91 -1.51
12 7 1.5 2 18.89 19.23 -0.336
4 8 2 3 8.49 8.8 -0.3136
2 9 2 1 23.16 22.57 0.5862
7 10 1.5 1 12.73 11.91 0.8202
3 11 1 3 12.35 11.91 0.4346
13 12 1.5 2 14.81 14.25 0.5637
11 13 1.5 2 25.8 26.05 -0.2502
Figure 2. DP analytical plots of (a) Predicted versus actual plot; (b) Normal plot of residuals
Figure 3. DP analytical plots of (c) Residuals versus the experimental run; and (d) pretreatment Residuals versus predicted 3.4 Optimization of NaOH pretreatment using CCD Provisionally, Figure 2(a) was presented in a graph of
the relationship between predicted versus actual plot in result
of R2 of 0.9849. And Figure 2(b) was presented in a graph of the externally studentized residuals versus predicted values to solution the standard deviations for normal probability. A finalizing of plots were showed in Figure 3 (c) and (d) of the residuals versus experimental run numbers were utilized to
estimate the fit of the model using an internally studentized construction of SS model.
Figure 4. Plots attained from SS model of DP (a) Contour plot NaOH-Time interaction, (b) 3D plot NaOH-Time interaction In these results, Contour and 3D plots are utilized to
indicate the interactions of two independent factors and the simultaneous significance. Therefore, two factors are measured as study variables with the interaction of variable factors being evaluated on the response value. The results from Figure 4 (a) and (b) of SS model at pretreatment by 2%
NaOH for 3 days, DP is 6.16, it operates with Table 1 that in the sample consists of oligosaccharides, malto- oligosaccharides, Maltodextrin, other oligosaccharides, Raffinose, stachyose and fructooligosaccharide. Which is good for enzymatic degradation in the next process, in the same direction as past research of Manmai et al. The highest TS and RS at the sample of pretreatment by 2% NaOH for 3 days (Manmai et al., 2019b), (Manmai et al., 2020). But on the other hands in sample was pretreated by 1%NaOH for 1 day, DP of 25.80, it can be predicted in this sample contains polysaccharides as lignocellulose.
Conclusion
In this study indicates that concentration of NaOH and time of reaction are impotent on the process of sugar producing for lignocellulose plants because in low concentration of NaOH (1%) and shot time (1 day) of pretreatment can be seen that example, lignocellulose has not yet been converted to oligosaccharides as the sample was
pretreated by 2% NaOH for 3 days. Which oligosaccharides are good for the step of hydrolysis by enzyme to prepare fermentable sugar. Consequently, Sunflower is an agricultural waste for the renewable and low-cost biomass for biofuel and it can be the alternative materials of the future.
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